An automated sampling importance resampling procedure for estimating parameter uncertainty
An automated sampling importance resampling procedure for estimating parameter uncertainty
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DOI：
10.1007/s10928-017-9542-0
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发表时间：
2017-12-01
影响因子：
2.5
通讯作者：
Karlsson, Mats O.
中科院分区：
文献类型：
--
作者：
Dosne, Anne-Gaelle;Bergstrand, Martin;Karlsson, Mats O.
在药物开发决策中，对终点周围的不确定性进行量化是至关重要的。在非线性混合效应模型（NLMEM）分析中，这种不确定性来源于模型参数周围的不确定性。评估参数不确定性的方法多种多样，但对其充分性的审查很少。在先前的出版物中，采样重要性重采样（SIR）被提出作为一种快速和轻假设的参数不确定性估计方法。SIR的非迭代实现证明对于一组简单的NLMEM是足够的，但是SIR设置的选择仍然是一个问题。在目前的工作中，通过开发一个自动化的、迭代的SIR过程，这个问题得到了缓解。新程序在25个真实数据示例上进行了测试，这些数据示例涵盖了广泛的药代动力学和药效学NLMEM，具有连续和分类的终点，具有多达39个估计参数和不同的数据丰富度。SIR平均在3次迭代后得到合适的结果。SIR还与协方差矩阵、自举和随机模拟和估计（SSE）进行了比较。SIR比bootstrap快10倍。SIR导致的相对标准误差类似于协方差矩阵和SSE。SIR参数95%置信区间也表现出与SSE相似的不对称性。综上所述，自动化SIR程序成功地应用于各种情况，其在PsN程序中的用户友好实现使NLMEM中参数不确定性的有效估计成为可能。
Quantifying the uncertainty around endpoints used for decision-making in drug development is essential. In nonlinear mixed-effects models (NLMEM) analysis, this uncertainty is derived from the uncertainty around model parameters. Different methods to assess parameter uncertainty exist, but scrutiny towards their adequacy is low. In a previous publication, sampling importance resampling (SIR) was proposed as a fast and assumption-light method for the estimation of parameter uncertainty. A non-iterative implementation of SIR proved adequate for a set of simple NLMEM, but the choice of SIR settings remained an issue. This issue was alleviated in the present work through the development of an automated, iterative SIR procedure. The new procedure was tested on 25 real data examples covering a wide range of pharmacokinetic and pharmacodynamic NLMEM featuring continuous and categorical endpoints, with up to 39 estimated parameters and varying data richness. SIR led to appropriate results after 3 iterations on average. SIR was also compared with the covariance matrix, bootstrap and stochastic simulations and estimations (SSE). SIR was about 10 times faster than the bootstrap. SIR led to relative standard errors similar to the covariance matrix and SSE. SIR parameter 95% confidence intervals also displayed similar asymmetry to SSE. In conclusion, the automated SIR procedure was successfully applied over a large variety of cases, and its user-friendly implementation in the PsN program enables an efficient estimation of parameter uncertainty in NLMEM.